OpenRaiser/Intern-Atlas huggingface dataset:
A New Paradigm of Software Service Engineering in the Era of Big Data and Big Service
Venue: arXiv cs.SE 2016
Year: 2016

Servitization is one of the most significant trends that reshapes the information world and society in recent years. The requirement of collecting,storing, processing, and sharing of the Big Data has led to massive software resources being developed and made accessible as web-based services to facilitate such process. These services that handle the Big Data come from various domains and heterogeneous networks, and converge into a huge complicated service network (or ecosystem), called the Big Service.The key issue facing the big data and big service ecosystem is how to optimally configure and operate the related service resources to serve the specific requirements of possible applications, i.e., how to reuse the existing service resources effectively and efficiently to develop the new applications or software services, to meet the massive individualized requirements of end-users.Based on analyzing the big service ecosystem, we present in this paper a new paradigm for software service engineering, RE2SEP (Requirement-Engineering Two-Phase of Service Engineering Paradigm), which includes three components: service-oriented requirement engineering, domain-oriented service engineering, and software service development approach. RE2SEP enables the rapid design and implementation of service solutions to match the requirement propositions of massive individualized customers in the Big Service ecosystem. A case study on people's mobility service in a smart city environment is given to demonstrate the application of RE2SEP.RE2SEP can potentially revolutionize the traditional life-cycle oriented software engineering, leading to a new approach to software service engineering.
Multi-Class Multi-Object Tracking using Changing Point Detection
Venue: arXiv cs.CV 2016
Year: 2016

This paper presents a robust multi-class multi-object tracking (MCMOT) formulated by a Bayesian filtering framework. Multi-object tracking for unlimited object classes is conducted by combining detection responses and changing point detection (CPD) algorithm. The CPD model is used to observe abrupt or abnormal changes due to a drift and an occlusion based spatiotemporal characteristics of track states. The ensemble of convolutional neural network (CNN) based object detector and Lucas-Kanede Tracker (KLT) based motion detector is employed to compute the likelihoods of foreground regions as the detection responses of different object classes. Extensive experiments are performed using lately introduced challenging benchmark videos; ImageNet VID and MOT benchmark dataset. The comparison to state-of-the-art video tracking techniques shows very encouraging results.
BreakID: Static Symmetry Breaking for ASP (System Description)
Venue: arXiv cs.AI 2016
Year: 2016

Symmetry breaking has been proven to be an efficient preprocessing technique for satisfiability solving (SAT). In this paper, we port the state-of-the-art SAT symmetry breaker BreakID to answer set programming (ASP). The result is a lightweight tool that can be plugged in between the grounding and the solving phases that are common when modelling in ASP. We compare our tool with sbass, the current state-of-the-art symmetry breaker for ASP.
Intelligent Pinning Based Cooperative Secondary Control of Distributed Generators for Microgrid in Islanding Operation Mode
Venue: arXiv cs.SY 2016
Year: 2016

Motivated by the fact that the location(s) and structural properties of the pinning node(s) affect the algebraic connectivity of a network with respect to the reference value and thereby, its dynamic performance, this paper studies the application of intelligent single and multiple pinning of distributed cooperative secondary control of distributed generators (DGs) in islanded microgrid operation. It is shown that the intelligent selection of a pinning set based on the degree of connectivity and distance of leader DG(s) from the rest of the network improves the transient performance for microgrid voltage and frequency regulation. The efficacy of the distributed control strategy based on the proposed algorithms is illustrated via numerical results simulating typical scenarios for a variety of microgrid configurations.
New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty
Venue: arXiv cs.CV 2016
Year: 2016

Multidimensional imaging techniques provide powerful ways to examine various kinds of scientific questions. The routinely produced datasets in the terabyte-range, however, can hardly be analyzed manually and require an extensive use of automated image analysis. The present thesis introduces a new concept for the estimation and propagation of uncertainty involved in image analysis operators and new segmentation algorithms that are suitable for terabyte-scale analyses of 3D+t microscopy images.
Placing Arrows in Directed Graph Drawings
Venue: arXiv cs.DS 2016
Year: 2016

We consider the problem of placing arrow heads in directed graph drawings without them overlapping other drawn objects. This gives drawings where edge directions can be deduced unambiguously. We show hardness of the problem, present exact and heuristic algorithms, and report on a practical study.
Profitable Task Allocation in Mobile Cloud Computing
Venue: arXiv cs.NI 2016
Year: 2016

We propose a game theoretic framework for task allocation in mobile cloud computing that corresponds to offloading of compute tasks to a group of nearby mobile devices. Specifically, in our framework, a distributor node holds a multidimensional auction for allocating the tasks of a job among nearby mobile nodes based on their computational capabilities and also the cost of computation at these nodes, with the goal of reducing the overall job completion time. Our proposed auction also has the desired incentive compatibility property that ensures that mobile devices truthfully reveal their capabilities and costs and that those devices benefit from the task allocation. To deal with node mobility, we perform multiple auctions over adaptive time intervals. We develop a heuristic approach to dynamically find the best time intervals between auctions to minimize unnecessary auctions and the accompanying overheads. We evaluate our framework and methods using both real world and synthetic mobility traces. Our evaluation results show that our game theoretic framework improves the job completion time by a factor of 2-5 in comparison to the time taken for executing the job locally, while minimizing the number of auctions and the accompanying overheads. Our approach is also profitable for the nearby nodes that execute the distributor's tasks with these nodes receiving a compensation higher than their actual costs.
Multi-Person Pose Estimation with Local Joint-to-Person Associations
Venue: arXiv cs.CV 2016
Year: 2016

Despite of the recent success of neural networks for human pose estimation, current approaches are limited to pose estimation of a single person and cannot handle humans in groups or crowds. In this work, we propose a method that estimates the poses of multiple persons in an image in which a person can be occluded by another person or might be truncated. To this end, we consider multi-person pose estimation as a joint-to-person association problem. We construct a fully connected graph from a set of detected joint candidates in an image and resolve the joint-to-person association and outlier detection using integer linear programming. Since solving joint-to-person association jointly for all persons in an image is an NP-hard problem and even approximations are expensive, we solve the problem locally for each person. On the challenging MPII Human Pose Dataset for multiple persons, our approach achieves the accuracy of a state-of-the-art method, but it is 6,000 to 19,000 times faster.
Applying Naive Bayes Classification to Google Play Apps Categorization
Venue: arXiv cs.LG 2016
Year: 2016

There are over one million apps on Google Play Store and over half a million publishers. Having such a huge number of apps and developers can pose a challenge to app users and new publishers on the store. Discovering apps can be challenging if apps are not correctly published in the right category, and, in turn, reduce earnings for app developers. Additionally, with over 41 categories on Google Play Store, deciding on the right category to publish an app can be challenging for developers due to the number of categories they have to choose from. Machine Learning has been very useful, especially in classification problems such sentiment analysis, document classification and spam detection. These strategies can also be applied to app categorization on Google Play Store to suggest appropriate categories for app publishers using details from their application. In this project, we built two variations of the Naive Bayes classifier using open metadata from top developer apps on Google Play Store in other to classify new apps on the store. These classifiers are then evaluated using various evaluation methods and their results compared against each other. The results show that the Naive Bayes algorithm performs well for our classification problem and can potentially automate app categorization for Android app publishers on Google Play Store
Probabilistically Robust SWIPT for Secrecy MISOME Systems
Venue: arXiv cs.IT 2016
Year: 2016

This paper considers simultaneous wireless information and power transfer (SWIPT) in a multiple-input single-output (MISO) downlink system consisting of one multi-antenna transmitter, one single-antenna information receiver (IR), multiple multi-antenna eavesdroppers (Eves) and multiple single-antenna energy-harvesting receivers (ERs). The main objective is to keep the probability of the legitimate user's achievable secrecy rate outage as well as the ERs' harvested energy outage caused by channel state information (CSI) uncertainties below some prescribed thresholds. As is well known, the secrecy rate outage constraints present a significant analytical and computational challenge. Incorporating the energy harvesting (EH) outage constraints only intensifies that challenge. In this paper, we address this challenging issue using convex restriction approaches which are then proved to yield rank-one optimal beamforming solutions. Numerical results reveal the effectiveness of the proposed schemes.
Bitonic st-orderings for Upward Planar Graphs
Venue: arXiv cs.DS 2016
Year: 2016

Canonical orderings serve as the basis for many incremental planar drawing algorithms. All these techniques, however, have in common that they are limited to undirected graphs. While $st$-orderings do extend to directed graphs, especially planar $st$-graphs, they do not offer the same properties as canonical orderings. In this work we extend the so called bitonic $st$-orderings to directed graphs. We fully characterize planar $st$-graphs that admit such an ordering and provide a linear-time algorithm for recognition and ordering. If for a graph no bitonic $st$-ordering exists, we show how to find in linear time a minimum set of edges to split such that the resulting graph admits one. With this new technique we are able to draw every upward planar graph on $n$ vertices by using at most one bend per edge, at most $n - 3$ bends in total and within quadratic area.
Using parallelism techniques to improve sequential and multi-core sorting performance
Venue: arXiv cs.DS 2016
Year: 2016

We propose new sequential sorting operations by adapting techniques and methods used for designing parallel sorting algorithms. Although the norm is to parallelize a sequential algorithm to improve performance, we adapt a contrarian approach: we employ parallel computing techniques to speed up sequential sorting. Our methods can also work for multi-core sorting with minor adjustments that do not necessarily require full parallelization of the original sequential algorithm. The proposed approach leads to the development of asymptotically efficient deterministic and randomized sorting operations whose practical sequential and multi-core performance, as witnessed by an experimental study, matches or surpasses existing optimized sorting algorithm implementations. We utilize parallel sorting techniques such as deterministic regular sampling and random oversampling. We extend the notion of deterministic regular sampling into deterministic regular oversampling for sequential and multi-core sorting and demonstrate its potential. We then show how these techniques can be used for sequential sorting and also lead to better multi-core sorting algorithm performance as witnessed by the undertaken experimental study.
Tunable QoS-Aware Network Survivability
Venue: arXiv cs.NI 2016
Year: 2016

Coping with network failures has been recognized as an issue of major importance in terms of social security, stability and prosperity. It has become clear that current networking standards fall short of coping with the complex challenge of surviving failures. The need to address this challenge has become a focal point of networking research. In particular, the concept of \textbf{\emph{tunable survivability}} offers major performance improvements over traditional approaches. Indeed, while the traditional approach aims at providing full (100\%) protection against network failures through disjoint paths, it was realized that this requirement is too restrictive in practice. Tunable survivability provides a quantitative measure for specifying the desired level (0\%-100\%) of survivability and offers flexibility in the choice of the routing paths. Previous work focused on the simpler class of "bottleneck" criteria, such as bandwidth. In this study, we focus on the important and much more complex class of \emph{additive} criteria, such as delay and cost. First, we establish some (in part, counter-intuitive) properties of the optimal solution. Then, we establish efficient algorithmic schemes for optimizing the level of survivability under additive end-to-end QoS bounds. Subsequently, through extensive simulations, we show that, at the price of \emph{negligible} reduction in the level of survivability, a major improvement (up to a factor of $2$) is obtained in terms of end-to-end QoS performance. Finally, we exploit the above findings in the context of a network design problem, in which, for a given investment budget, we aim to improve the survivability of the network links.
Measuring Machine Intelligence Through Visual Question Answering
Venue: arXiv cs.AI 2016
Year: 2016

As machines have become more intelligent, there has been a renewed interest in methods for measuring their intelligence. A common approach is to propose tasks for which a human excels, but one which machines find difficult. However, an ideal task should also be easy to evaluate and not be easily gameable. We begin with a case study exploring the recently popular task of image captioning and its limitations as a task for measuring machine intelligence. An alternative and more promising task is Visual Question Answering that tests a machine's ability to reason about language and vision. We describe a dataset unprecedented in size created for the task that contains over 760,000 human generated questions about images. Using around 10 million human generated answers, machines may be easily evaluated.
Towards Concolic Testing for Hybrid Systems
Venue: arXiv cs.SE 2016
Year: 2016

Hybrid systems exhibit both continuous and discrete behavior. Analyzing hybrid systems is known to be hard. Inspired by the idea of concolic testing (of programs), we investigate whether we can combine random sampling and symbolic execution in order to effectively verify hybrid systems. We identify a sufficient condition under which such a combination is more effective than random sampling. Furthermore, we analyze different strategies of combining random sampling and symbolic execution and propose an algorithm which allows us to dynamically switch between them so as to reduce the overall cost. Our method has been implemented as a web-based checker named HyChecker. HyChecker has been evaluated with benchmark hybrid systems and a water treatment system in order to test its effectiveness.
Hybrid and Subexponential Linear Logics Technical Report
Venue: arXiv cs.LO 2016
Year: 2016

HyLL (Hybrid Linear Logic) and SELL (Subexponential Linear Logic) are logical frameworks that have been extensively used for specifying systems that exhibit modalities such as temporal or spatial ones. Both frameworks have linear logic (LL) as a common ground and they admit (cut-free) complete focused proof systems. The difference between the two logics relies on the way modalities are handled. In HyLL, truth judgments are labelled by worlds and hybrid connectives relate worlds with formulas. In SELL, the linear logic exponentials (!, ?) are decorated with labels representing locations, and an ordering on such labels defines the provability relation among resources in those locations. It is well known that SELL, as a logical framework, is strictly more expressive than LL. However, so far, it was not clear whether HyLL is more expressive than LL and/or SELL. In this paper, we show an encoding of the HyLL's logical rules into LL with the highest level of adequacy, hence showing that HyLL is as expressive as LL. We also propose an encoding of HyLL into SELL $\doublecup$ (SELL plus quantification over locations) that gives better insights about the meaning of worlds in HyLL. We conclude our expressiveness study by showing that previous attempts of encoding Computational Tree Logic (CTL) operators into HyLL cannot be extended to consider the whole set of temporal connectives. We show that a system of LL with fixed points is indeed needed to faithfully encode the behavior of such temporal operators.
Hash2Vec, Feature Hashing for Word Embeddings
Venue: arXiv cs.CL 2016
Year: 2016

In this paper we propose the application of feature hashing to create word embeddings for natural language processing. Feature hashing has been used successfully to create document vectors in related tasks like document classification. In this work we show that feature hashing can be applied to obtain word embeddings in linear time with the size of the data. The results show that this algorithm, that does not need training, is able to capture the semantic meaning of words. We compare the results against GloVe showing that they are similar. As far as we know this is the first application of feature hashing to the word embeddings problem and the results indicate this is a scalable technique with practical results for NLP applications.
PDDL+ Planning via Constraint Answer Set Programming
Venue: arXiv cs.AI 2016
Year: 2016

PDDL+ is an extension of PDDL that enables modelling planning domains with mixed discrete-continuous dynamics. In this paper we present a new approach to PDDL+ planning based on Constraint Answer Set Programming (CASP), i.e. ASP rules plus numerical constraints. To the best of our knowledge, ours is the first attempt to link PDDL+ planning and logic programming. We provide an encoding of PDDL+ models into CASP problems. The encoding can handle non-linear hybrid domains, and represents a solid basis for applying logic programming to PDDL+ planning. As a case study, we consider the EZCSP CASP solver and obtain promising results on a set of PDDL+ benchmark problems.
Human Pose Estimation in Space and Time using 3D CNN
Venue: arXiv cs.CV 2016
Year: 2016

This paper explores the capabilities of convolutional neural networks to deal with a task that is easily manageable for humans: perceiving 3D pose of a human body from varying angles. However, in our approach, we are restricted to using a monocular vision system. For this purpose, we apply a convolutional neural network approach on RGB videos and extend it to three dimensional convolutions. This is done via encoding the time dimension in videos as the 3\ts{rd} dimension in convolutional space, and directly regressing to human body joint positions in 3D coordinate space. This research shows the ability of such a network to achieve state-of-the-art performance on the selected Human3.6M dataset, thus demonstrating the possibility of successfully representing temporal data with an additional dimension in the convolutional operation.
Good Enough Practices in Scientific Computing
Venue: arXiv cs.SE 2016
Year: 2016

We present a set of computing tools and techniques that every researcher can and should adopt. These recommendations synthesize inspiration from our own work, from the experiences of the thousands of people who have taken part in Software Carpentry and Data Carpentry workshops over the past six years, and from a variety of other guides. Unlike some other guides, our recommendations are aimed specifically at people who are new to research computing.
BLISlab: A Sandbox for Optimizing GEMM
Venue: arXiv cs.MS 2016
Year: 2016

Matrix-matrix multiplication is a fundamental operation of great importance to scientific computing and, increasingly, machine learning. It is a simple enough concept to be introduced in a typical high school algebra course yet in practice important enough that its implementation on computers continues to be an active research topic. This note describes a set of exercises that use this operation to illustrate how high performance can be attained on modern CPUs with hierarchical memories (multiple caches). It does so by building on the insights that underly the BLAS-like Library Instantiation Software (BLIS) framework by exposing a simplified "sandbox" that mimics the implementation in BLIS. As such, it also becomes a vehicle for the "crowd sourcing" of the optimization of BLIS. We call this set of exercises BLISlab.
A Likelihood Ratio Detector for Identifying Within-Perimeter Computer Network Attacks
Venue: arXiv cs.CR 2016
Year: 2016

The rapid detection of attackers within firewalls of enterprise computer net- works is of paramount importance. Anomaly detectors address this problem by quantifying deviations from baseline statistical models of normal network behav- ior and signaling an intrusion when the observed data deviates significantly from the baseline model. However, many anomaly detectors do not take into account plausible attacker behavior. As a result, anomaly detectors are prone to a large number of false positives due to unusual but benign activity. This paper first in- troduces a stochastic model of attacker behavior which is motivated by real world attacker traversal. Then, we develop a likelihood ratio detector that compares the probability of observed network behavior under normal conditions against the case when an attacker has possibly compromised a subset of hosts within the network. Since the likelihood ratio detector requires integrating over the time each host be- comes compromised, we illustrate how to use Monte Carlo methods to compute the requisite integral. We then present Receiver Operating Characteristic (ROC) curves for various network parameterizations that show for any rate of true posi- tives, the rate of false positives for the likelihood ratio detector is no higher than that of a simple anomaly detector and is often lower. We conclude by demon- strating the superiority of the proposed likelihood ratio detector when the network topologies and parameterizations are extracted from real-world networks.